48 lines
2.0 KiB
Python
48 lines
2.0 KiB
Python
#!/usr/bin/env python3
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"""COMPARISON REPORT"""
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print("""
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\033[1;36m============================================================\033[0m
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\033[1;36m GLYPHOS vs TRANSFORMER - COMPARATIVE ANALYSIS \033[0m
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\033[1;36m============================================================\033[0m
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\033[1;33m1. WHAT EACH BENCHMARK MEASURES\033[0m
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------------------------------------------------------------
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\033[1mTRANSFORMER:\033[0m
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✓ Full vocabulary embedding (10,000+ classes)
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✓ Multi-head attention O(N²) for ALL token pairs
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✓ Softmax normalization (exponential operations)
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✓ Residual connections + Layer Normalization
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✓ Language model output head
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\033[1mGLYPHOS:\033[0m
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✓ Sparse graph with 4 edges per node
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✓ Simple weighted averaging of neighbors
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✓ Binary similarity check (fixed threshold)
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✓ Sigmoid activation (cheap approximation)
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✓ No vocabulary, no language modeling
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\033[91mKEY POINT: These solve fundamentally DIFFERENT problems!\033[0m
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\033[1;33m2. OPERATIONAL COST COMPARISON\033[0m
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------------------------------------------------------------
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| Component | Transformer | GlyphOS |
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|--------------------|------------------|------------------|
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| Attention Ops | \033[91m~500M/token\033[0m | \033[92m~16K/node\033[0m |
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| Memory Pattern | \033[91mRandom/Cache miss\033[0m|\033[92m Sequential/Clean\033[0m|
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| Scaling Behavior | \033[91mO(N²)\033[0m | \033[92mO(edges) ≈ O(N)\033[0m |
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| Training Required | \033[91mYes (weeks)\033[0m | \033[92mNo (static)\033[0m |
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| Capability | \033[93mText generation\033[0m | \033[93mGraph relaxation\033[0m |
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\033[1;33m3. APPLES-TO-APPLES COMPARISON NEEDS\033[0m
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------------------------------------------------------------
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□ Same task (e.g., text completion)
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✓ Same sequence length
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✓ Same hardware
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□ Same evaluation metric (perplexity, BLEU, etc.)
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□ Same parameter budget
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\033[91mWithout these, performance claims are misleading.\033[0m
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\033[36mRun actual benchmarks to see real timings.\033[0m
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""")
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